Prediction of Remaining Life of Aircraft Engines Based on BiLSTM-GRU-Attention Model

This study aims to enhance the prediction precision of aircraft engine remaining useful life (RUL) by overcoming common challenges in current models, such as ineffective feature extraction and insufficient modeling of long-term temporal dependencies. We propose a novel multilayer hybrid architecture that combines bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU) networks, augmented with an attention mechanism to enhance the model's focus on informative temporal patterns. In this framework, raw time series data are initially processed by the BiLSTM to extract bidirectional features associated with engine health conditions. The GRU network is subsequently used to effectively model long-range dependencies, thereby enriching the temporal representation. An adaptive attention module is included to assign varying importance to different features, allowing the model to focus on key indicators of engine condition. Evaluation results on the FD001 and FD003 datasets show that the model achieves root mean squared error reductions ranging from 8.81% to 30.60% and from 7.48% to 37.96%, validating its performance and robustness in RUL forecasting. In comparison with conventional BiLSTM and GRU models, the proposed BiLSTM-GRU-Attention architecture integrates attention-based feature weighting with a hybrid recurrent framework, thereby offering a concise and effective approach to RUL prediction for aircraft engines.